Long-term forecasting of El Niño events via dynamic factor simulations

Li, Mengheng; Koopman, Siem Jan; Lit, Rutger; Petrova, Desislava

doi:10.1016/j.jeconom.2019.05.004

Cited by 7 publications

(4 citation statements)

References 46 publications

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“…Based on the availability, the monthly data collection has been prioritised and later in case of absence of monthly data the yearly data has been interpolated over the selected period in various ways based on the characteristics of the data. Because of the interpolation of the yearly data, which is a frequent practice in forecasting (Zhao et al, 2019;Tokumitsu et al, 2015) and even in simulation and data generation (Li et al, 2020), the outliers in the data set have been eroded automatically before starting the analysis. Aligning or removing outliers as a logical pre-processing step of data analysis brings in benefits by avoiding a few bad apples that may spoil the entire bushel (Cant and Xu, 2020; Shah and Patil, 2019;Lyutikova and Shmatova, 2020).…”

Section: Econophysics Methodologymentioning

confidence: 99%

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

Sakalayen

Duru

Hirata

2020

MABR

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Purpose Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting. Design/methodology/approach By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit. Findings The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model. Originality/value By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

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Section: Econophysics Methodologymentioning

confidence: 99%

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

Sakalayen

Duru

Hirata

2020

MABR

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“…In the case of the El Niño phenomenon, some works focused on understanding the effects of the ENSO phases around the world [13], [14], [24] and possible connections with other climate or different phenomena [1]- [3], [12], [24], [25]. In terms of ML applications, several works seek to predict or forecast long-term ENSO episodes [15]- [19], [26], [27]. They select a group of spatiotemporal datasets and information to feed the forecasting model and perform some regression analyses.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies have reported statistical and ML approaches to understand the ENSO phenomenon [13]- [15]. Most of the works concentrate on the long-term forecasting/regression problem [15]- [19], which is a hard and challenging problem FIGURE 1. Schematic of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Classifying El Niño-Southern Oscillation Combining Network Science and Machine Learning

et al. 2020

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Machine learning and complex network theory have emerged as crucial tools to extract meaningful information from big data, especially those related to complex systems. In this work, we aim to combine them to analyze El Niño Southern Oscillation (ENSO) phases. This non-linear phenomenon consists of anomalous (de)increase of temperature at the tropical Pacific Ocean, which has irregular occurrence and causes climatic variability worldwide. We construct temporal Climate Networks from the Surface Air Temperature time-series and calculate network metrics to characterize the warm and cold ENSO episodes. The metrics are used as topological features for classification. We employ ten classifiers and achieved 80% AUC ROC when predicting the intensity of Strong/ Weak El Niño and Strong/ Weak La Niña for the next season. The complex network represents the relationship among different regions of the planet and machine learning creates models to classify the different classes of ENSO. This work opens new paths of research by integrating network science and machine learning to analyze complex data like global climate systems.

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“…State space models have already been used, in the econometric literature, for modeling time series of environmental variables. Proietti and Hillebrand (2017) employ them to model seasonal changes in central England temperatures, Bennedsen et al (2019) to analyse the trend of the airborne fraction and sink rate of anthropogenically released carbon dioxide, Li et al (2020) to forecast El Niño events, and Hillebrand et al (2020) to study the relation between global mean sea level and surface temperature.…”

Section: Introductionmentioning

confidence: 99%

Multivariate state space methods for official statistics and climate modelling

Schiavoni

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Long-term forecasting of El Niño events via dynamic factor simulations

Cited by 7 publications

References 46 publications

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

Classifying El Niño-Southern Oscillation Combining Network Science and Machine Learning

Multivariate state space methods for official statistics and climate modelling

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